We Need Actionable Intelligence Not Artificial

Why was the best performing stock in the S&P 500 compared to a flywheel?

What is the flywheel model for growth?

Why is data critical to high performance?

What has changed to make data accessible and actionable for any firm?

By mastering data and embedding intelligence into its organization and innovation any firm can now aspire to become a high performer, no matter how modest its past or unfashionable its sector.

Netflix was the best performing stock in the S&P 500 in 2015. For the key to Netflix’s relentless growth see Why media titans would be wise not to ignore Netflix. The New York Times article inevitably compares Netflix to Amazon: ‘Netflix, like Amazon, is a flywheel that keeps spinning faster: As it gets more subscribers, it gets more data and more money to fund more content, which in turn helps it bring in more customers, and on and on, ever faster.’

Jim Collins developed the flywheel metaphor in his must-read ‘Good to Great’. Collins’ research team studied over 1400 firms and identified 11 that after years and sometimes decades of unspectacular achievement started to grow rapidly and significantly outperform their competition.

Discipline – and data – is central to the Jim Collins Flywheel model

Collins had no time for grand gestures like the change management or digital disruption initiatives beloved of management consultants. His Flywheel results from the additive effect of many small actions, implemented with discipline, and acting on each other like compound interest.

According to Wikipedia a flywheel is ‘a rotating mechanical device that is used to store rotational energy.’ The flywheel provides continuous energy even though the rate of power to the system is uneven. What is the energy that makes the flywheels spin at Netflix, Amazon and other fast-growth enterprises? How is it that energy and growth are relentless and uninterrupted by the efforts of competition or state of the economy?

The energy in business today is its data. Again from the New York Times: ‘Like Amazon, (Netflix) is amassing a cache of intelligence on what customers want, and it’s using that data to create content that appeals to a wide range of demographics globally.’

Netflix can create content for audiences no other studio can identify

Wall Street has still not fully assimilated the paradigm shift to a new business model based on customer value, engagement and data rather than product shipments, efficiencies and margins.

Mastery of data is not limited to ‘digital’ firms. In his research Davenport found a relentless focus on data at ‘traditional’ firms as diverse as Harrahs (casinos), Capital One (finance) and Gallo (wine). Today we can see the same data-driven flywheel of growth powering Kroger (supermarkets), Under Armour (sportswear) and Xiaomi from China (mobile).

Mastery of data is not limited to large companies. Collins’s work demonstrated how any company no matter how modest its past or unfashionable its industry can outperform its competition and the market. It is now no longer the case that only the largest firms can master their data to create a flywheel for growth.

Only a minority of firms have connected their data to grow their businesses

Every firm is awash in data or as a recent survey noted, overloaded with data and light on insight. Yet only a small percentage of firms are confident in their capability to master their data. What are the barriers preventing more firms from creating data-driven growth flywheels?

The big data ecosystem has been dominated by technology solutions that require often inhibiting investments in hardware and software. Most of these solutions have been proprietary and adversarial. There have been too many offerings, ludicrously 2,000 plus in sales and marketing alone.

Data science has been in vogue but the focus has been on high profile artificial intelligence demonstrations and programmatic media algorithms.

Although awash in their own data firms also found themselves paying others for data, in particular publishers, agencies and data management platforms to run their increasingly algorithmic media.

Now we need actionable intelligence, not artificial or algorithmic.

The processing speed and power to manage the data sets of most mid-size firms is now available on local servers. or cloud platforms at a cost of a few hundreds of dollars a month.

The technology required is open source, low cost, accessible through easily connected components, and rapid to implement. Different systems can be connected through API’s to create straight through intelligence. The notion of competing full stack solutions is becoming archaic.

The data that matters is small not big. The primary source is proprietary, a firm’s own customers. The best customers – the fans – are walking, talking, sharing and shopping generators of data. A strategy for growth starts with engaging fans as individuals and in their communities.

The data science is powerful when it is framed and interpreted by people with domain knowledge. What we can call a human in the loop approach embeds intelligence in the firm’s applications and workflow.

The measured, disciplined application of data and science to top-line growth and bottom-line efficiency creates the flywheel for growth

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Stewart Pearson is a Global Strategist, Economist and Statistician with 20 years experience working across the Americas, Europe, Middle East, Africa and Asia Pacific. As Vice Chairman and Chief Client Officer at the #1 digital, data and CRM agency he architected teams and programs in over 60 markets. Stewart ’s passion to help firms become data-driven flywheels and outperform their markets been a constant throughout his career. Stewart@consilient-group.com